Land grabbing and land concentration: Mapping changing patterns of farmland ownership in three rural municipalities in Saskatchewan, Canada
Bibliographic record
Abstract
Since the 2007-2008 global food crisis there is growing interest in changing patterns of farmland ownership. Utilizing a dataset of the names of all farmland titleholders along with GIS data mapping software, this article demonstrates changes in patterns of land ownership in three rural municipalities (RMs) in Saskatchewan, Canada. A diverse mix of new actors have entered the farmland market in the past decade or two, with some now owning more than 100,000 acres each in the province. Our research reveals a list of the investment companies, pension plans, and large farmer/investor hybrids buying land and also maps investment activity and large land transactions in the three RMs. While 7.8% to 13.1% of the farmland is now owned by “land grabbers”, our study also found a significant rise in land concentration in the hands of farmers when compared to 20 years ago. For example, in one RM the four largest landowners—a mix of farmers and investment companies and farmer/investor hybrids—now own 28% of the land. We then discuss some initial findings concerning the impact changing patterns of land ownership is having on the cohesion and vitality of communities and conclude with a series of questions for further research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".